Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	943
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	111.7 KiB
Average record size in memory	121.3 B

Variable types

DateTime	1
Categorical	8
Numeric	7

Warnings

`username` has constant value "levie"	Constant
`tweet` has a high cardinality: 943 distinct values	High cardinality
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`photos` is highly correlated with `urls` and 1 other fields	High correlation
`bins` is highly correlated with `percent change`	High correlation
`urls` is highly correlated with `photos`	High correlation
`replies_count` is highly correlated with `likes_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`percent change` is highly correlated with `bins`	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`bins` is highly correlated with `username`	High correlation
`urls` is highly correlated with `username`	High correlation
`cashtags` is highly correlated with `username`	High correlation
`hashtags` is highly correlated with `username`	High correlation
`username` is highly correlated with `bins` and 5 other fields	High correlation
`video` is highly correlated with `username`	High correlation
`mentions` is highly correlated with `username`	High correlation
`tweet` is uniformly distributed	Uniform
`date` has unique values	Unique
`tweet` has unique values	Unique
`photos` has 820 (87.0%) zeros	Zeros
`replies_count` has 28 (3.0%) zeros	Zeros
`retweets_count` has 52 (5.5%) zeros	Zeros
`percent change` has 19 (2.0%) zeros	Zeros

Reproduction

Analysis started	2021-09-27 19:02:55.560063
Analysis finished	2021-09-27 19:03:05.860271
Duration	10.3 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

date
Date

UNIQUE

Distinct	943
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

Minimum	2016-08-27 09:30:00
Maximum	2021-07-20 16:00:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	943
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.	1
@saurabhbhatia Completely agree on both of these! Hope you begin to see these soon-ish...	1
If I owned this desk I would be asleep 93% of the working day. https://t.co/eAXPPuOTiG	1
I have yet to run into a business strategy problem that can’t be solved by a pyramid shape or a flywheel. After seeing this TikTok deal play out, I will never again say “ok that’s not realistic” after an episode of Billions.	1
This is extremely impressive. A well-timed digital strategy that now let’s them own their consumer relationship end-to-end.	1
Other values (938)	938

Length

Max length	2175
Median length	144
Mean length	208.6680806
Min length	3

Characters and Unicode

Total characters	196774
Distinct characters	175
Distinct categories	16 ?
Distinct scripts	3 ?
Distinct blocks	12 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	943 ?
Unique (%)	100.0%

Sample

1st row	Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.
2nd row	The Pope's face is one of those, "Shit, it's just a statue of a drone, I thought I was getting a real one" faces. https://t.co/AIURrVdXTt
3rd row	6,000 people registered for BoxWorks next week! We'll be making major product announcements. And some Trump jokes. https://t.co/EXn9NZAbk0
4th row	Unlike essentially every other kind of challenge a startup runs into, you probably don't hear "it's not rocket science" much at SpaceX.
5th row	Hopefully it's clear by now that promising taco trucks on every corner would, in fact, be the most electable platform to run on.

Common Values

Value	Count	Frequency (%)
Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.	1	0.1%
@saurabhbhatia Completely agree on both of these! Hope you begin to see these soon-ish...	1	0.1%
If I owned this desk I would be asleep 93% of the working day. https://t.co/eAXPPuOTiG	1	0.1%
I have yet to run into a business strategy problem that can’t be solved by a pyramid shape or a flywheel. After seeing this TikTok deal play out, I will never again say “ok that’s not realistic” after an episode of Billions.	1	0.1%
This is extremely impressive. A well-timed digital strategy that now let’s them own their consumer relationship end-to-end.	1	0.1%
You have to hand it to Facebook. They sure do know how to compete when it matters most. https://t.co/F0d58HRIKV	1	0.1%
@mcannonbrookes Nah man, fits your portfolio much better... https://t.co/IA4Nl0le9Y Are you really even a tech company if you haven’t explored acquiring TikTok?	1	0.1%
Plenty of startup opportunities appear to be in “crowded markets” when in reality the market is just customers solving problems in different ways all looking for a better solution.	1	0.1%
We all miss you and are sorry about how the internet played out.	1	0.1%
My back of the envelope math says that people are more excited by Kamala Harris than they were Tim Kaine. Boom	1	0.1%
Other values (933)	933	98.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	1216	3.7%
to	1035	3.2%
a	645	2.0%
and	588	1.8%
of	552	1.7%
is	539	1.7%
in	441	1.4%
this	423	1.3%
for	385	1.2%
that	358	1.1%
Other values (5655)	26306	81.0%

Most occurring characters

Value	Count	Frequency (%)
	31923	16.2%
e	17494	8.9%
t	14185	7.2%
o	12715	6.5%
a	11171	5.7%
n	10215	5.2%
i	10110	5.1%
s	9472	4.8%
r	9112	4.6%
l	6434	3.3%
Other values (165)	63943	32.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	147762	75.1%
Space Separator	31926	16.2%
Other Punctuation	7042	3.6%
Uppercase Letter	6730	3.4%
Decimal Number	1680	0.9%
Final Punctuation	728	0.4%
Other Symbol	262	0.1%
Dash Punctuation	253	0.1%
Initial Punctuation	104	0.1%
Connector Punctuation	67	< 0.1%
Other values (6)	220	0.1%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
😂	33	12.6%
😀	28	10.7%
🙏	24	9.2%
👏	17	6.5%
☁	12	4.6%
👍	7	2.7%
😉	5	1.9%
❤	5	1.9%
😄	5	1.9%
⬇	5	1.9%
Other values (69)	121	46.2%

Lowercase Letter

Value	Count	Frequency (%)
e	17494	11.8%
t	14185	9.6%
o	12715	8.6%
a	11171	7.6%
n	10215	6.9%
i	10110	6.8%
s	9472	6.4%
r	9112	6.2%
l	6434	4.4%
h	6350	4.3%
Other values (17)	40504	27.4%

Uppercase Letter

Value	Count	Frequency (%)
T	744	11.1%
I	699	10.4%
A	472	7.0%
S	460	6.8%
B	458	6.8%
W	443	6.6%
C	313	4.7%
M	251	3.7%
P	234	3.5%
N	231	3.4%
Other values (16)	2425	36.0%

Other Punctuation

Value	Count	Frequency (%)
.	2478	35.2%
,	1136	16.1%
/	942	13.4%
@	865	12.3%
:	514	7.3%
!	316	4.5%
'	287	4.1%
"	152	2.2%
*	115	1.6%
?	103	1.5%
Other values (4)	134	1.9%

Decimal Number

Value	Count	Frequency (%)
0	450	26.8%
1	277	16.5%
2	241	14.3%
5	128	7.6%
9	108	6.4%
3	105	6.2%
6	98	5.8%
4	95	5.7%
8	95	5.7%
7	83	4.9%

Math Symbol

Value	Count	Frequency (%)
+	17	53.1%
~	8	25.0%
=	5	15.6%
≠	1	3.1%
⤵	1	3.1%

Space Separator

Value	Count	Frequency (%)
	31923	> 99.9%
	3	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	227	89.7%
—	26	10.3%

Final Punctuation

Value	Count	Frequency (%)
’	628	86.3%
”	100	13.7%

Initial Punctuation

Value	Count	Frequency (%)
“	102	98.1%
‘	2	1.9%

Open Punctuation

Value	Count	Frequency (%)
(	41	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	49	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	66	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	67	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	27	100.0%

Format

Value	Count	Frequency (%)
‍	5	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	154492	78.5%
Common	42250	21.5%
Inherited	32	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	31923	75.6%
.	2478	5.9%
,	1136	2.7%
/	942	2.2%
@	865	2.0%
’	628	1.5%
:	514	1.2%
0	450	1.1%
!	316	0.7%
'	287	0.7%
Other values (110)	2711	6.4%

Latin

Value	Count	Frequency (%)
e	17494	11.3%
t	14185	9.2%
o	12715	8.2%
a	11171	7.2%
n	10215	6.6%
i	10110	6.5%
s	9472	6.1%
r	9112	5.9%
l	6434	4.2%
h	6350	4.1%
Other values (43)	47234	30.6%

Inherited

Value	Count	Frequency (%)
️	27	84.4%
‍	5	15.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	195615	99.4%
Punctuation	863	0.4%
Emoticons	141	0.1%
None	87	< 0.1%
VS	27	< 0.1%
Misc Symbols	17	< 0.1%
Dingbats	9	< 0.1%
Enclosed Alphanum Sup	6	< 0.1%
Latin 1 Sup	5	< 0.1%
Letterlike Symbols	2	< 0.1%
Other values (2)	2	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	31923	16.3%
e	17494	8.9%
t	14185	7.3%
o	12715	6.5%
a	11171	5.7%
n	10215	5.2%
i	10110	5.2%
s	9472	4.8%
r	9112	4.7%
l	6434	3.3%
Other values (75)	62784	32.1%

Emoticons

Value	Count	Frequency (%)
😂	33	23.4%
😀	28	19.9%
🙏	24	17.0%
😉	5	3.5%
😄	5	3.5%
😳	4	2.8%
😬	4	2.8%
😎	4	2.8%
😢	3	2.1%
😟	3	2.1%
Other values (20)	28	19.9%

Latin 1 Sup

Value	Count	Frequency (%)
	3	60.0%
é	2	40.0%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇺	3	50.0%
🇸	2	33.3%
🇪	1	16.7%

None

Value	Count	Frequency (%)
👏	17	19.5%
👍	7	8.0%
⬇	5	5.7%
🚀	5	5.7%
🔥	4	4.6%
🤪	3	3.4%
🎉	3	3.4%
👇	3	3.4%
🤯	3	3.4%
🧐	3	3.4%
Other values (28)	34	39.1%

Math Operators

Value	Count	Frequency (%)
≠	1	100.0%

Misc Symbols

Value	Count	Frequency (%)
☁	12	70.6%
♂	2	11.8%
♀	2	11.8%
⚔	1	5.9%

VS

Value	Count	Frequency (%)
️	27	100.0%

Dingbats

Value	Count	Frequency (%)
❤	5	55.6%
✅	3	33.3%
✊	1	11.1%

Punctuation

Value	Count	Frequency (%)
’	628	72.8%
“	102	11.8%
”	100	11.6%
—	26	3.0%
‍	5	0.6%
‘	2	0.2%

Letterlike Symbols

Value	Count	Frequency (%)
™	2	100.0%

Sup Arrows B

Value	Count	Frequency (%)
⤵	1	100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

levie	943

Length

Max length	5
Median length	5
Mean length	5
Min length	5

Characters and Unicode

Total characters	4715
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	levie
2nd row	levie
3rd row	levie
4th row	levie
5th row	levie

Common Values

Value	Count	Frequency (%)
levie	943	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
levie	943	100.0%

Most occurring characters

Value	Count	Frequency (%)
e	1886	40.0%
l	943	20.0%
v	943	20.0%
i	943	20.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4715	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1886	40.0%
l	943	20.0%
v	943	20.0%
i	943	20.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4715	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1886	40.0%
l	943	20.0%
v	943	20.0%
i	943	20.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4715	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1886	40.0%
l	943	20.0%
v	943	20.0%
i	943	20.0%

mentions
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

0	866
1	58
2	17
4	1
3	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	943
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.2%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	943	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	943	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	943	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	866	91.8%
1	58	6.2%
2	17	1.8%
4	1	0.1%
3	1	0.1%

hashtags
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

0	932
1	11

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	943
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

Most occurring characters

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	943	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	943	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	943	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	932	98.8%
1	11	1.2%

cashtags
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

0	941
1	2

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	943
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

Most occurring characters

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	943	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	943	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	943	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	941	99.8%
1	2	0.2%

video
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

0	808
1	128
2	5
4	1
3	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	943
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.2%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	943	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	943	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	943	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	808	85.7%
1	128	13.6%
2	5	0.5%
4	1	0.1%
3	1	0.1%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.1527041357

Minimum	0
Maximum	7
Zeros	820
Zeros (%)	87.0%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	7
Range	7
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.4630859798
Coefficient of variation (CV)	3.032569993
Kurtosis	58.70723629
Mean	0.1527041357
Median Absolute Deviation (MAD)	0
Skewness	5.657196384
Sum	144
Variance	0.2144486247
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	820	87.0%
1	110	11.7%
2	10	1.1%
7	1	0.1%
3	1	0.1%
4	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	820	87.0%
1	110	11.7%
2	10	1.1%
3	1	0.1%
4	1	0.1%
7	1	0.1%

Value	Count	Frequency (%)
7	1	0.1%
4	1	0.1%
3	1	0.1%
2	10	1.1%
1	110	11.7%
0	820	87.0%

urls
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.5 KiB

0	785
1	152
2	6

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	943
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

Most occurring characters

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	943	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	943	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	943	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	785	83.2%
1	152	16.1%
2	6	0.6%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	165
Distinct (%)	17.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	42.69141039

Minimum	0
Maximum	1373
Zeros	28
Zeros (%)	3.0%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	11
median	23
Q3	47.5
95-th percentile	143
Maximum	1373
Range	1373
Interquartile range (IQR)	36.5

Descriptive statistics

Standard deviation	71.43682494
Coefficient of variation (CV)	1.673330168
Kurtosis	137.4067264
Mean	42.69141039
Median Absolute Deviation (MAD)	16
Skewness	8.903154451
Sum	40258
Variance	5103.219958
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	31	3.3%
13	31	3.3%
0	28	3.0%
5	24	2.5%
12	23	2.4%
3	23	2.4%
6	22	2.3%
14	22	2.3%
9	22	2.3%
8	20	2.1%
Other values (155)	697	73.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	28	3.0%
1	31	3.3%
2	18	1.9%
3	23	2.4%
4	17	1.8%
5	24	2.5%
6	22	2.3%
7	14	1.5%
8	20	2.1%
9	22	2.3%

Value	Count	Frequency (%)
1373	1	0.1%
658	1	0.1%
514	1	0.1%
449	1	0.1%
402	1	0.1%
379	1	0.1%
315	1	0.1%
298	1	0.1%
296	1	0.1%
257	1	0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	398
Distinct (%)	42.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	216.4220573

Minimum	0
Maximum	8667
Zeros	52
Zeros (%)	5.5%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	32
median	95
Q3	227
95-th percentile	803.9
Maximum	8667
Range	8667
Interquartile range (IQR)	195

Descriptive statistics

Standard deviation	442.1322995
Coefficient of variation (CV)	2.042916998
Kurtosis	149.1851339
Mean	216.4220573
Median Absolute Deviation (MAD)	79
Skewness	9.369359817
Sum	204086
Variance	195480.9703
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	52	5.5%
1	21	2.2%
27	11	1.2%
46	10	1.1%
2	10	1.1%
14	9	1.0%
13	9	1.0%
12	9	1.0%
23	8	0.8%
17	8	0.8%
Other values (388)	796	84.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	52	5.5%
1	21	2.2%
2	10	1.1%
3	7	0.7%
4	6	0.6%
5	3	0.3%
6	6	0.6%
7	2	0.2%
8	6	0.6%
9	6	0.6%

Value	Count	Frequency (%)
8667	1	0.1%
3392	1	0.1%
3205	1	0.1%
2479	1	0.1%
2401	1	0.1%
2110	1	0.1%
2016	1	0.1%
2013	1	0.1%
1951	1	0.1%
1931	1	0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	780
Distinct (%)	82.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1565.697773

Minimum	0
Maximum	47412
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	0
5-th percentile	26.1
Q1	370.5
median	826
Q3	1786
95-th percentile	5187.5
Maximum	47412
Range	47412
Interquartile range (IQR)	1415.5

Descriptive statistics

Standard deviation	2667.338067
Coefficient of variation (CV)	1.703609798
Kurtosis	109.318219
Mean	1565.697773
Median Absolute Deviation (MAD)	559
Skewness	8.091379877
Sum	1476453
Variance	7114692.362
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	6	0.6%
55	4	0.4%
4	4	0.4%
1092	4	0.4%
752	4	0.4%
15	4	0.4%
7	4	0.4%
133	4	0.4%
8	3	0.3%
557	3	0.3%
Other values (770)	903	95.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	2	0.2%
3	1	0.1%
4	4	0.4%
5	6	0.6%
6	1	0.1%
7	4	0.4%
8	3	0.3%
9	3	0.3%

Value	Count	Frequency (%)
47412	1	0.1%
28419	1	0.1%
21918	1	0.1%
17469	1	0.1%
15575	1	0.1%
13656	1	0.1%
11068	1	0.1%
10889	1	0.1%
10593	1	0.1%
10479	1	0.1%

number of tweets
Real number (ℝ_≥0)

Distinct	15
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.80699894

Minimum	1
Maximum	15
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	2
95-th percentile	5
Maximum	15
Range	14
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.601775092
Coefficient of variation (CV)	0.8864283522
Kurtosis	18.18554396
Mean	1.80699894
Median Absolute Deviation (MAD)	0
Skewness	3.628235866
Sum	1704
Variance	2.565683447
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
1	594	63.0%
2	183	19.4%
3	74	7.8%
4	40	4.2%
5	21	2.2%
6	10	1.1%
7	7	0.7%
8	4	0.4%
10	3	0.3%
12	2	0.2%
Other values (5)	5	0.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	594	63.0%
2	183	19.4%
3	74	7.8%
4	40	4.2%
5	21	2.2%
6	10	1.1%
7	7	0.7%
8	4	0.4%
9	1	0.1%
10	3	0.3%

Value	Count	Frequency (%)
15	1	0.1%
14	1	0.1%
13	1	0.1%
12	2	0.2%
11	1	0.1%
10	3	0.3%
9	1	0.1%
8	4	0.4%
7	7	0.7%
6	10	1.1%

price
Real number (ℝ_≥0)

Distinct	732
Distinct (%)	77.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.67166823

Minimum	9.06000042
Maximum	29.25
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.5 KiB

Quantile statistics

Minimum	9.06000042
5-th percentile	14.17400004
Q1	16.91333357
median	18.13499928
Q3	20.32208308
95-th percentile	24.79199924
Maximum	29.25
Range	20.18999958
Interquartile range (IQR)	3.408749501

Descriptive statistics

Standard deviation	3.122952749
Coefficient of variation (CV)	0.1672562253
Kurtosis	0.575870231
Mean	18.67166823
Median Absolute Deviation (MAD)	1.534998894
Skewness	0.4414069218
Sum	17607.38314
Variance	9.752833875
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
20.88999939	6	0.6%
18.79999924	6	0.6%
18.36000061	5	0.5%
18.55999947	5	0.5%
18.04999924	5	0.5%
16.92000008	4	0.4%
17.14999962	4	0.4%
17.37999916	4	0.4%
17.54999924	4	0.4%
17.79999924	4	0.4%
Other values (722)	896	95.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
9.06000042	1	0.1%
9.119999886	1	0.1%
9.210000038	1	0.1%
9.229999542	1	0.1%
9.590000153	1	0.1%
9.756666819	1	0.1%
10.45333322	1	0.1%
10.60000038	1	0.1%
10.93000031	1	0.1%
11.5	1	0.1%

Value	Count	Frequency (%)
29.25	1	0.1%
28.42000008	1	0.1%
28	1	0.1%
27.35000038	1	0.1%
27.31999969	1	0.1%
27.21250057	1	0.1%
27.20999972	1	0.1%
27.20000076	1	0.1%
26.82500029	1	0.1%
26.7750001	1	0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	921
Distinct (%)	97.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.001000680484

Minimum	-0.1438071139
Maximum	0.2148070316
Zeros	19
Zeros (%)	2.0%
Negative	438
Negative (%)	46.4%
Memory size	7.5 KiB

Quantile statistics

Minimum	-0.1438071139
5-th percentile	-0.0295389959
Q1	-0.00768489594
median	0.000870460276
Q3	0.009556436548
95-th percentile	0.03048687252
Maximum	0.2148070316
Range	0.3586141455
Interquartile range (IQR)	0.01724133249

Descriptive statistics

Standard deviation	0.02125458754
Coefficient of variation (CV)	21.24013396
Kurtosis	16.50105948
Mean	0.001000680484
Median Absolute Deviation (MAD)	0.008582620687
Skewness	1.12081547
Sum	0.9436416968
Variance	0.0004517574914
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	19	2.0%
0.007084989273	2	0.2%
0.02461536114	2	0.2%
0.001618930285	2	0.2%
-0.005875462913	2	0.2%
-0.0005817468628	1	0.1%
-0.001663896327	1	0.1%
0.003888871935	1	0.1%
-0.001660142767	1	0.1%
0.003335156571	1	0.1%
Other values (911)	911	96.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.1438071139	1	0.1%
-0.08737868173	1	0.1%
-0.07826083639	1	0.1%
-0.07729682187	1	0.1%
-0.07436016785	1	0.1%
-0.06822073405	1	0.1%
-0.0661435143	1	0.1%
-0.06083515091	1	0.1%
-0.0599103952	1	0.1%
-0.05968925344	1	0.1%

Value	Count	Frequency (%)
0.2148070316	1	0.1%
0.1130333949	1	0.1%
0.1119176284	1	0.1%
0.1091000575	1	0.1%
0.09020216115	1	0.1%
0.08215483381	1	0.1%
0.07986306695	1	0.1%
0.07846779712	1	0.1%
0.06603331817	1	0.1%
0.06311823747	1	0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

no change	764
rise	93
drop	86

Length

Max length	9
Median length	9
Mean length	8.050901379
Min length	4

Characters and Unicode

Total characters	7592
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	no change
2nd row	no change
3rd row	no change
4th row	rise
5th row	no change

Common Values

Value	Count	Frequency (%)
no change	764	81.0%
rise	93	9.9%
drop	86	9.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	764	44.8%
change	764	44.8%
rise	93	5.4%
drop	86	5.0%

Most occurring characters

Value	Count	Frequency (%)
n	1528	20.1%
e	857	11.3%
o	850	11.2%
	764	10.1%
c	764	10.1%
h	764	10.1%
a	764	10.1%
g	764	10.1%
r	179	2.4%
i	93	1.2%
Other values (3)	265	3.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6828	89.9%
Space Separator	764	10.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	1528	22.4%
e	857	12.6%
o	850	12.4%
c	764	11.2%
h	764	11.2%
a	764	11.2%
g	764	11.2%
r	179	2.6%
i	93	1.4%
s	93	1.4%
Other values (2)	172	2.5%

Space Separator

Value	Count	Frequency (%)
	764	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6828	89.9%
Common	764	10.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	1528	22.4%
e	857	12.6%
o	850	12.4%
c	764	11.2%
h	764	11.2%
a	764	11.2%
g	764	11.2%
r	179	2.6%
i	93	1.4%
s	93	1.4%
Other values (2)	172	2.5%

Common

Value	Count	Frequency (%)
	764	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7592	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	1528	20.1%
e	857	11.3%
o	850	11.2%
	764	10.1%
c	764	10.1%
h	764	10.1%
a	764	10.1%
g	764	10.1%
r	179	2.4%
i	93	1.2%
Other values (3)	265	3.5%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	date	tweet	username	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
0	2016-08-27 09:30:00	Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.	levie	0	0	0	31	147	577	1	13.130	0.006130	no change
1	2016-08-29 16:00:00	The Pope's face is one of those, "Shit, it's just a statue of a drone, I thought I was getting a real one" faces. https://t.co/AIURrVdXTt	levie	1	1	0	8	111	338	1	13.290	0.018391	no change
2	2016-08-30 16:00:00	6,000 people registered for BoxWorks next week! We'll be making major product announcements. And some Trump jokes. https://t.co/EXn9NZAbk0	levie	0	0	1	5	34	192	1	13.320	0.000000	no change
3	2016-09-01 16:00:00	Unlike essentially every other kind of challenge a startup runs into, you probably don't hear "it's not rocket science" much at SpaceX.	levie	0	0	0	20	355	928	1	13.980	0.055094	rise
4	2016-09-02 16:00:00	Hopefully it's clear by now that promising taco trucks on every corner would, in fact, be the most electable platform to run on.	levie	0	0	0	8	186	650	1	14.140	0.010000	no change
5	2016-09-03 09:30:00	BoxWorks is now 5 days away! Grab a free expo pass to join us for the keynotes here: https://t.co/4Pgi6yBzXa https://t.co/4CtLndUeUz	levie	1	1	1	18	64	329	1	14.035	-0.007426	no change
6	2016-09-04 09:30:00	Building an enterprise software company is 95% technology and 5% channeling your inner Taylor Swift. https://t.co/vrXrXt55Rr	levie	1	1	0	20	138	503	1	14.070	-0.006531	no change
7	2016-09-05 09:30:00	"Oh sure, this seems like a totally safe and normal Airbnb to stay in." https://t.co/vfulGB7ftx	levie	1	1	0	13	83	479	1	14.105	-0.005640	no change
8	2016-09-06 09:30:00	7,000 people registered for BoxWorks this week! If you can't make it (why!? 😢😟😞😗) we'll be streaming live here: https://t.co/EXn9NZAbk0	levie	0	0	1	6	38	158	1	14.140	-0.004751	no change
9	2016-09-07 16:00:00	Steps for a great morning: 1. Load https://t.co/OefY681YEc 2. Load https://t.co/MFBvDV5Hnj 3. Experience all your tech wishes come true	levie	0	0	2	7	28	155	1	14.520	0.013967	no change

Last rows

	date	tweet	username	mentions	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
933	2021-07-06 16:00:00	@zlurie Adding $55B in market cap. Heck of a first day on the job!	levie	0	0	0	0	1	1	34	1	26.340000	-0.018994	no change
934	2021-07-07 09:30:00	Could not be more proud or excited for how the Box team is cranking on our product roadmap right now. Lots and lots of epic innovation coming soon for customers this summer and fall!	levie	0	0	0	0	12	6	236	1	26.500000	0.006074	no change
935	2021-07-07 16:00:00	Excited to partner with BT as they continue to leverage the Box Content Cloud! https://t.co/lJQ2PIQSas	levie	0	0	0	1	1	1	55	1	25.610001	-0.033585	drop
936	2021-07-09 09:30:00	@apoorva_mehta @fidjissimo Congratttttttts!	levie	0	0	0	0	0	0	6	1	25.110001	-0.000398	no change
937	2021-07-11 16:00:00	Crazy https://t.co/dRQ7MSW2B4	levie	0	1	1	0	23	105	1479	1	24.843333	-0.015586	no change
938	2021-07-14 16:00:00	If you’re not killing products you’re not working on enough risky ideas. Interesting to see which states are all for free markets, until they’re not.	levie	0	0	0	0	39	188	1936	2	23.840000	-0.018930	no change
939	2021-07-17 16:00:00	@DavidSacks Will text him now @DavidSacks Left here, reporting for duty. For sure came from a lab. @micsolana @rabois Never said mass censorship 😉. Said mass distribution has consequences. I think plenty of innovation starts out great and then we see the follow on effects of it, so it’s worth being critical and introspective of this. Also, FWIW I have no solution to offer, just complaints 😀 @alanknit @rabois Well I guess Keith wins this round, yet again! @peteryared @rabois @raohackr Unless you’ve been going to Lauren Boebert rallies, you might not be the demographic where our issue lies. But also on category #2 that just is a bad public health strategy. @rabois @raohackr Notice I didn’t say Scientists™. I said science 😀. Just talking about vaccines here. @raohackr @rabois Yeah, not going to work in a heavily politicized public health crisis where large groups willfully ignore science. Speech vs. speech isn’t going to solve anything. Anyway, it is what it is 😀. @raohackr @rabois There are 0 things Biden admin could do to convince a meaningful portion of US to get vaccinated when it has become fully politicized and weaponized as an issue. They are a centralized entity fighting a decentralized war 10X their size with the same informational tools. @rabois I don’t think the government should control what’s on social media. Largely, I think social media platforms should decide. But I also think we don’t fully understand how we’re supposed to resolve the consequence of mass distribution of dangerous content. @rabois Alternatively: if people are mad about everyone having a bazooka, just wait until they realize what pistols are used for. And bow and arrows. And cutlery. @rabois Come on though 😀. 17 years into social media we can thoughtfully differentiate small group communication from mass distribution. If we can’t critically think about the consequences of that, we really don’t understand what tech unleashes on the world.	levie	0	0	0	0	48	13	638	11	23.303333	-0.009773	no change
940	2021-07-19 09:30:00	@rowantrollope @ericsyuan 🥳🎉🥂 congrats you two!	levie	1	0	0	0	1	1	16	1	23.260000	0.005331	no change
941	2021-07-20 09:30:00	Amazing https://t.co/YGDtOVvm92	levie	0	1	1	0	3	6	145	1	23.129999	0.006966	no change
942	2021-07-20 16:00:00	This is a very sad take on entrepreneurship Space innovation from many ventures is just plain win-win-win. More funding of research that will lead to unexpected discoveries, more rocket scientists, more knowledge about the universe, and of course the possibility of tourism and expanding to other planets.	levie	0	0	0	0	63	80	970	2	23.740000	0.026373	rise

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